Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vasilyu1983/AI-Agents-public --skill dev-ai-coding-metricsgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vasilyu1983/ai-agents-public/dev-ai-coding-metrics)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/dev-ai-coding-metrics"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/dev-ai-coding-metrics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/dev-ai-coding-metrics"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/dev-ai-coding-metrics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.02574 |
| Opus 5 | $0.00018 | $0.01287 |
| Sonnet 5 | $0.00007 | $0.00515 |
| Haiku 4.5 | $0.00004 | $0.00257 |
Grade A, and why
dev-ai-coding-metrics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Coding Metrics
Measures coding assistants and coding agents without collapsing results into vanity metrics or one blended score.
The critical distinction is mode: assistants help inline or in chat; agents execute multi-step work and need task-level measurement. Do not measure them as if they were the same thing.
When to Use This Skill
| Trigger | Example |
|---|---|
| Designing a pilot or rollout scorecard | "We're rolling out Copilot to 200 engineers — what do we measure?" |
| Diagnosing usage-up / outcomes-flat | "Seat utilization is 80% but PR throughput is unchanged" |
| Comparing assistant vs. agent workflows | "Should we instrument these separately?" |
| Building an ROI model or leadership report | "Finance wants a renewal decision by Q3" |
| Designing an experiment better than vendor benchmarks | "We can't trust the vendor's numbers — how do we run our own study?" |
Defaults
| Rule | Rationale |
|---|---|
| Start from the decision, not the telemetry available | Prevents instrument-what-is-easy bias |
| Separate assistant and agent funnels | Mixing hides which workflow drives results |
| Pair every speed metric with quality + experience | Speed alone is misleading |
| Aggregate at team level | Individual dashboards become surveillance |
| Treat benchmarks as capability signals, not business KPIs | Benchmark gaps do not equal production gaps |
Workflow
- Define the decision.
- Pick the program mode: assistant, agent, or mixed.
- Build the minimum viable scorecard.
- Choose the study design.
- Produce one deliverable.
ASCII Flow
AI coding metrics request
-> decision to support: buy, renew, improve, prove, or diagnose
-> split mode: assistant, agent, or mixed
-> select scorecard families: adoption, delivery, quality, economics, experience
-> choose study design and baseline window
-> collect team-level and task-level evidence
-> report confidence, sample size, and confounds
-> deliver ROI model, dashboard, experiment plan, or executive report
What ships with it
22 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 382 B
- assets/adoption-survey-template.md 8.2 KB
- assets/executive-report-template.md 9.2 KB
- assets/experiment-design-template.md 9.0 KB
- assets/metric-dashboard-template.md 9.5 KB
- assets/roi-calculator-template.md 6.2 KB
- data/sample-ai-metrics.json 3.4 KB
- data/sources.json 25 KB
- learnings.consolidated.md 597 B
- learnings.md 385 B
- references/adoption-metrics.md 10 KB
- references/agent-execution-metrics.md 11 KB
- references/benchmarking-methodology.md 21 KB
- references/developer-experience-metrics.md 19 KB
- references/evidence-update.md 7.4 KB
- references/productivity-metrics.md 9.3 KB
- references/quality-metrics.md 23 KB
- references/roi-framework.md 11 KB
- references/theory-of-constraints-applied.md 44 KB
- scripts/extract_github_events.py 11 KB runs code
- scripts/README.md 4.2 KB
- scripts/roi_calculator.py 18 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 194 lines · 36 tokens per session scan A 9990aeb15b88
dev-ai-coding-metrics is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 7d ago), licensed MIT. It adds 36 tokens to every session and 2,574 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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